Constructing Optimal Noise Channels for Enhanced Robustness in Quantum Machine Learning

no code implementations • 25 Apr 2024 • David Winderl, Nicola Franco, Jeanette Miriam Lorenz

With the rapid advancement of Quantum Machine Learning (QML), the critical need to enhance security measures against adversarial attacks and protect QML models becomes increasingly evident.

Quantum Machine Learning

Quantum Neural Networks under Depolarization Noise: Exploring White-Box Attacks and Defenses

no code implementations • 29 Nov 2023 • David Winderl, Nicola Franco, Jeanette Miriam Lorenz

Leveraging the unique properties of quantum mechanics, Quantum Machine Learning (QML) promises computational breakthroughs and enriched perspectives where traditional systems reach their boundaries.

Adversarial Robustness Multi-class Classification +1

Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness

1 code implementation • 30 Apr 2023 • Nicola Franco, Tom Wollschläger, Benedikt Poggel, Stephan Günnemann, Jeanette Miriam Lorenz

We conduct a detailed analysis for the decomposition of MILP with Benders and Dantzig-Wolfe methods.

Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection

1 code implementation • 27 Mar 2023 • Nicola Franco, Daniel Korth, Jeanette Miriam Lorenz, Karsten Roscher, Stephan Guennemann

As the use of machine learning continues to expand, the importance of ensuring its safety cannot be overstated.

Adversarial Robustness Out-of-Distribution Detection